Cargando…
Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for charac...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479843/ https://www.ncbi.nlm.nih.gov/pubmed/30978981 http://dx.doi.org/10.3390/s19071737 |
_version_ | 1783413438625611776 |
---|---|
author | Li, Mengxuan Tian, Shanshan Sun, Linlin Chen, Xi |
author_facet | Li, Mengxuan Tian, Shanshan Sun, Linlin Chen, Xi |
author_sort | Li, Mengxuan |
collection | PubMed |
description | Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann–Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice. |
format | Online Article Text |
id | pubmed-6479843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64798432019-04-29 Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method Li, Mengxuan Tian, Shanshan Sun, Linlin Chen, Xi Sensors (Basel) Article Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann–Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice. MDPI 2019-04-11 /pmc/articles/PMC6479843/ /pubmed/30978981 http://dx.doi.org/10.3390/s19071737 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Mengxuan Tian, Shanshan Sun, Linlin Chen, Xi Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method |
title | Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method |
title_full | Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method |
title_fullStr | Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method |
title_full_unstemmed | Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method |
title_short | Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method |
title_sort | gait analysis for post-stroke hemiparetic patient by multi-features fusion method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6479843/ https://www.ncbi.nlm.nih.gov/pubmed/30978981 http://dx.doi.org/10.3390/s19071737 |
work_keys_str_mv | AT limengxuan gaitanalysisforpoststrokehemipareticpatientbymultifeaturesfusionmethod AT tianshanshan gaitanalysisforpoststrokehemipareticpatientbymultifeaturesfusionmethod AT sunlinlin gaitanalysisforpoststrokehemipareticpatientbymultifeaturesfusionmethod AT chenxi gaitanalysisforpoststrokehemipareticpatientbymultifeaturesfusionmethod |